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of 70
pro vyhledávání: '"Horácek, B. Milan"'
Autor:
Zaman, Md Shakil, Dhamala, Jwala, Bajracharya, Pradeep, Sapp, John L., Horacek, B. Milan, Wu, Katherine C., Trayanova, Natalia A., Wang, Linwei
Probabilistic estimation of cardiac electrophysiological model parameters serves an important step towards model personalization and uncertain quantification. The expensive computation associated with these model simulations, however, makes direct Ma
Externí odkaz:
http://arxiv.org/abs/2110.06851
Estimation of patient-specific model parameters is important for personalized modeling, although sparse and noisy clinical data can introduce significant uncertainty in the estimated parameter values. This importance source of uncertainty, if left un
Externí odkaz:
http://arxiv.org/abs/2006.01983
The estimation of patient-specific tissue properties in the form of model parameters is important for personalized physiological models. However, these tissue properties are spatially varying across the underlying anatomical model, presenting a signi
Externí odkaz:
http://arxiv.org/abs/2005.07804
Autor:
Gyawali, Prashnna Kumar, Li, Zhiyuan, Knight, Cameron, Ghimire, Sandesh, Horacek, B. Milan, Sapp, John, Wang, Linwei
To improve the ability of VAE to disentangle in the latent space, existing works mostly focus on enforcing independence among the learned latent factors. However, the ability of these models to disentangle often decreases as the complexity of the gen
Externí odkaz:
http://arxiv.org/abs/1909.01839
Personalization of cardiac models involves the optimization of organ tissue properties that vary spatially over the non-Euclidean geometry model of the heart. To represent the high-dimensional (HD) unknown of tissue properties, most existing works re
Externí odkaz:
http://arxiv.org/abs/1907.01406
Autor:
Ghimire, Sandesh, Dhamala, Jwala, Gyawali, Prashnna Kumar, Sapp, John L, Horacek, B. Milan, Wang, Linwei
Publikováno v:
In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 508-516. Springer, Cham, 2018
Noninvasive reconstruction of cardiac transmembrane potential (TMP) from surface electrocardiograms (ECG) involves an ill-posed inverse problem. Model-constrained regularization is powerful for incorporating rich physiological knowledge about spatiot
Externí odkaz:
http://arxiv.org/abs/1905.04803
Akademický článek
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Autor:
Gyawali, Prashnna K, Knight, Cameron, Ghimire, Sandesh, Horacek, B. Milan, Sapp, John L., Wang, Linwei
While deep representation learning has become increasingly capable of separating task-relevant representations from other confounding factors in the data, two significant challenges remain. First, there is often an unknown and potentially infinite nu
Externí odkaz:
http://arxiv.org/abs/1811.00073
The increasing availability of electrocardiogram (ECG) data has motivated the use of data-driven models for automating various clinical tasks based on ECG data. The development of subject-specific models are limited by the cost and difficulty of obta
Externí odkaz:
http://arxiv.org/abs/1808.01524
Autor:
Zhou, Shijie, AbdelWahab, Amir, Sapp, John L., Sung, Eric, Aronis, Konstantinos N., Warren, James W., MacInnis, Paul J., Shah, Rushil, Horáček, B. Milan, Berger, Ronald, Tandri, Harikrishna, Trayanova, Natalia A., Chrispin, Jonathan
Publikováno v:
In JACC: Clinical Electrophysiology March 2021 7(3):395-407
